Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Scalable graph clustering using stochastic flows: applications to community discovery
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
GAIA: graph classification using evolutionary computation
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Semi-supervised feature selection for graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Semi-supervised clustering has recently received a lot of attention in the literature, which aims to improve the clustering performance with limited supervision. Most existing semi-supervised clustering studies assume that the data is represented in a vector space, e.g., text and relational data. When the data objects have complex structures, e.g., proteins and chemical compounds, those semi-supervised clustering methods are not directly applicable to clustering such graph objects. In this paper, we study the problem of semi-supervised clustering of data objects which are represented as graphs. The supervision information is in the form of pairwise constraints of must-links and cannot-links. As there is no predefined feature set for the graph objects, we propose to use discriminative subgraph patterns as the features. We design an objective function which incorporates the constraints to guide the subgraph feature mining and selection process. We derive an upper bound of the objective function based on which, a branch-and-bound algorithm is proposed to speedup subgraph mining. We also introduce a redundancy measure into the feature selection process in order to reduce the redundancy in the feature set. When the graph objects are represented in the vector space of the discriminative subgraph features, we use semi-supervised kernel K-means to cluster all graph objects. Experimental results on real-world protein datasets demonstrate that the constraint information can effectively guide the feature selection and clustering process and achieve satisfactory clustering performance.